Marc Tremblay

High resolution smart image sensor with integrated parallel analog processing for multiresolution edge extraction

Abstract This paper presents a vision sensor which generates a multiresolution edge description using parallel analog processing support. Its multimodule architecture is based on a Multi-port Access of photo-Receptor (MAR) hexagonal sensor coupled to an external but powerful analog processing unit and a microcoded digital interface. The system supports image scanning and edge tracking. Satellite analog processing allows extensive computation using VLSI technology, leaving all the sensor area available for photo-transduction and communication pathways. It is thus possible to design a sensor with up to 500 × 500 pixels on a single CMOS chip using 1.2 μm technology. The goal of the approach described here is to exploit an imbedded edge tracing algorithm in order to generate a scene description as a list of connected edge segments. Experimental results are presented for the current prototype which implements 256×256 pixels with corresponding multiresolution edge maps.

Motion vision sensor architecture with asynchronous self-signaling pixels

A custom CMOS imager with integrated motion computation is described. The architecture is based on correlating in time moving edges. Edges are located in time by a custom sensor; and correlated in a coprocessing module. The sensor architecture is centered around a compact pixel with analog signal processing and digital self-signaling capabilities. The sensor pixels detect moving edges in the image and communicate their position using an address-event protocol associated to temporal stamps. The coprocessing module correlates the edges and computes the velocity vector map. The motion sensor could be used in applications such as self-guided vehicles, mobile robotics and smart surveillance systems. The article details the motion sensor architecture, the simulated performance, the VLSI implementation and some preliminary results on fabricated prototypes.

A Focal Plane Architecture for Motion Computation

A new focal plane architecture for motion computation is presented. The design is based on the Smart Sensor paradigm: combining transduction and early processing at sensor level. The sensor computes focal plane motion and direction in a subsampled space, with programmable spatio-temporal bandwidth. The architecture is designed around an array of neuromorphic analog processing cells with local photo-transduction, computation of temporal variations in the image and correlation between neighbor pixels. The inherent process of serial read-out is used for further integration of low-level processing and reduction of complexity of each pixel. An external dedicated digital processor controls the system, interprets, and integrates the information from sets of four processing pixels in order to create a motion-based medium-level description of the image. The approach developed for VLSI implementation offers an excellent combination of small pixel area and a computationally efficient method for image motion measurement. The architecture is being implemented in a standard 1.5 ?m CMOS process.

Mixed-signal VLSI Architecture for Real-Time Computer Vision

This paper presents the architecture of a computer vision system targeted for real-time robot vision and pattern recognition applications. The proposed mixed-signal very large scale integration (VLSI) architecture integrates photo-transduction with low- and medium-level processing such as multi-resolution edge extraction, scale-space integration, edge tracking, dominant point extraction, and database generation. Its high performance stems from a custom CMOS smart image sensor providing parallel access to illuminance data and a set of parallel analog filters performing multi-resolution edge extraction. We have also developed a digital controller which manages data flow between the processing modules of the system and which constructs a database of the observed scene under the supervision of a digital signal processor (DSP) unit. This database describes relevant object contours as a linked list of linear segments and circular arcs with precomputed local and global properties. Such a token description of the scene is suitable for robot vision and pattern recognition applications, since it significantly compresses the amount of data to be processed by further high-level algorithms. Experimental results obtained with the current prototype of the system are very promising, with the complete process, from image acquisition to scene database creation, performed in less than a second.

Low level segmentation using CMOS smart hexagonal image sensor

The exploitation of analog VLSI techniques combined with computer vision knowledge offers spectacular possibilities. Limitations of current VLSI technologies do not allow to create sensors with extremely complex pixel architecture, but the coupling of external CMOS analog processing units is a great solution for rapid low level segmentation processes. This paper presents a novel sensing approach where photo-transduction, multiresolution feature extraction, scale-space integration, and edge tracking combined with sub-pixel interpolation are performed on a mixed-signal (digital-analog) VLSI architecture. The paper also discusses how we implement the curvature primal sketch into the system for higher level scene representation. The main sensory part of this integrated image acquisition system is a CMOS sensor called Multiport Access photo-Receptor (MAR). VLSI also provides means to integrate analog computing, digital controller, and DSP co-processor modules which define a powerful sensory chip set for focal plane image processing. A current version of the MAR sensor which implements 256/spl times/256 pixels includes 16 analog spatial filters which simultaneously compute multiresolution edge maps. This novel smart image sensor approach with associated low level segmentation capability presents good opportunities for real time automated process for the particular case of unstructured environment.

Transmission of the color information using quad-trees and segmentation-based approaches for the compression of color images with limited palette

The compression of color images is usually performed independently along each of the 3 axis of a luminance-chrominance color space. When applied to images using a limited color palette, it generates images which take on more values than those found in the original color palette. These images must be color quantized before they can be displayed with a limited palette. In this paper, we present two new approaches for the lossy compression of color quantized images that does not require color quantization of the decoded images. The algorithms restrict the pixels of the decoded image to take values only in the original color palette. The first algorithm does so by using lists of colors taken by pixels in variable block sizes. The second one uses a color segmentation of the image. These two approaches improve the performance of previously proposed algorithms. For comparable quality and similar bit rate, the proposed approaches have lower decoding complexity than standard DCT-based coding algorithms.<<ETX>>

A VLSI Implementation Of A Light Sensor With Imbedded Focal Plane Processing Capabilities

The complexity of information extraction requires the development of efficient and dedicated hardware systems designed to operate in harmony with robot vision and automation. This paper describes a system which combines optical sensing with integrated focal-plane processing capabilities and integrated image post-processing. It is capable of automatic edge tracking and returns lists of connected pixels. I. INTRODUCTION Autonomous robotic applications require the use of reliable sensors since the capability to extract meaningful information from the surrounding environment is paramount to the accomplishment of any task. Among all sensing modalities, computer vision is the one that is most naturally associated with autonomous robotics. This originates from the fact that visual information can provide a very rich semantic description of the world. Vision operates through a process of successive refinements. The illuminance recorded at each component of the retina is processed, and low-level features such as edges and regions are extracted through relatively simple operations. Higher-level percepts such as depth and motion parameters can then be obtained from these low-level features. A very effective approach to the extraction of edges is based on the convolution of circularly symmetric masks with the illuminance image El). Among the available circularly symmetric masks is the well-known Laplacian of Gaussian (LOG) expressed as:

Focal-plane VLSI processing for multiresolution edge extraction

The challenge of information extraction in robot vision and automated inspection requires the development of efficient and dedicated hardware systems. A specific requirement relates to the hierarchical description of a scene, which is difficult to implement in real-time on conventional computers. Hardware solutions may exploit parallel computing capabilities in order to provide intelligent sensing of visual information. A promising strategy seeks to exploit VLSI solutions in novel architectures for optical sensing and processing. The Multi- port Array photo-Receptor system (MAR) discussed in this paper combines optical transduction with integrated focal-plane processing. The central element of the MAR system is a full custom VLSI photo-sensor array with hexagonal tessellation which provides parallel analog read-out from groups of pixels over prescribed areas. The overall capability of the sensor is enhanced by the addition of external analog computation which performs real-time spatial convolution at multiple resolutions and uses feedback control for automatic edge tracking. Current VLSI technology allows the fabrication of a CMOS sensor array with dimensions of up to 500 X 500 pixels on a 1.5 cm die using CMOS 1.2 micron technology. VLSI also provides means to integrate analog computing modules and microcontrol capabilities. A set of chips required by the system has been fabricated and a first prototype which integrates an array of 128 X 128 pixels with zero-crossing detection at seven different spatial resolutions runs at a rate of 1 M pixel/sec. Edge data at multiple resolutions are computed in real-time. Parallel edge extraction at 16 different resolutions will be available from a forthcoming unit. The sensor includes arbitrary pixel displacement and non-linear dark current compensation. This type of integrated sensor is a good candidate for advanced applications which require small weight and size.

Z-scan studies of refractive index changes in azo-dye-doped PMMA films

We present measurements of complex refractive index changes of new azo dye doped PMMA system, using the so-called Z-scan technique. the photomodulation of the refractive index and absorption coefficients is studied both in steady-state and transient excitation regimes. The saturation intensity in AZD3-PMMA compound is also measured and corresponding corrections are made in obtained Z-scan data. The excitation and relaxation dynamics of the holographic recording are compared with above results. This comparison shows, that the complex refractive index variation (delta) n in AZD3-PMMA is resulted by combination of different excitation modes. However, the (delta) n does not change the sign upon continued exposition. This confirms that the non-monotone growth of holographic diffraction efficiency in this compound is resulted by dynamic saturation of holographic gratings.

VLSI architecture for the embedded extraction of dominant points on object contours

This paper presents a special-purpose VLSI architecture for dominant point extraction along 2D contours. Such dominant points carry useful information for shape analysis and pattern recognition applications since they represent a local shape property and segment object contours into piecewise linear segments and circular arcs. The proposed architecture implements an algorithm based on the curvature primal sketch. It consists of a set of 1D systolic FIR filters performing a multiresolution analysis of the scenes object contours, a set of finite-state-machines extracting zero-crossings and extrema of the filtered data, and a set of scale-space integration cells combining the accurate locations provided by the finest filters with the noise rejection properties of the coarsest ones in order to reliably extract relevant dominant points with accurate localization. The overall architecture has been successfully implemented and integrated to a custom machine vision system with real-time edge-extraction and edge-tracking capabilities. Some experimental results obtained using this system are presented and discussed. Performance issues are also addressed.